CN117293758B - Automatic protection method for digital distribution feeder monitoring terminal based on fault identification - Google Patents
Automatic protection method for digital distribution feeder monitoring terminal based on fault identification Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a digital distribution feeder monitoring terminal automatic protection method based on fault identification, which comprises the steps of acquiring initial abnormal load data with a time stamp of a distribution feeder in a preset time period; for any initial abnormal load data, acquiring the abnormal fluctuation degree of the initial abnormal load data, and acquiring target abnormal load data according to the abnormal fluctuation degree of all initial abnormal load data; aiming at any target abnormal load data, acquiring a continuous variation trend index and a frequency characteristic value of the target abnormal load data, and acquiring the early warning degree of the target abnormal load data according to the continuous variation trend index and the frequency characteristic value; the early warning degree of all the target abnormal load data is obtained, and the automatic protection of the digital power distribution feeder monitoring terminal is carried out according to the early warning degree of all the target abnormal load data, so that the monitoring accuracy of the digital power distribution feeder monitoring terminal is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an automatic protection method for a digital distribution feeder monitoring terminal based on fault identification.
Background
Distribution feeders refer to power transmission lines for transmitting power from a power plant or substation to a user terminal, which act as bridges connecting power sources and loads, enabling efficient transmission and distribution of power. The digital distribution feeder monitoring terminal is used for carrying out data acquisition and analysis on the distribution feeder by utilizing a digital technology so as to realize real-time monitoring on the distribution feeder, and particularly, the digital distribution feeder monitoring terminal is used for acquiring load data of the distribution feeder, analyzing information such as load change trend, load peak value and the like of a power plant or a transformer substation according to the load data, and further optimizing load scheduling and planning of the power plant or the transformer substation according to an analysis result so as to balance supply and demand relation and improve reliability and stability of a power grid.
In the prior art, a threshold method is generally adopted for monitoring the load of a distribution feeder, namely, collected load data is subjected to data analysis and processing to obtain the abnormal degree of the load data, the obtained abnormal degree of the load data is compared with a preset abnormal threshold value to obtain a comparison result, a digital distribution feeder monitoring terminal automatically monitors abnormal load changes, such as overload or unbalance, according to the comparison result, and when the load data higher than or lower than the abnormal threshold value occurs, the digital distribution feeder monitoring terminal triggers an alarm mechanism to remind operators to take corresponding measures. However, under the condition of actual monitoring, noise data points caused by environmental interference factors such as power supply fluctuation or electromagnetic interference or data acquisition transmission errors and the like exist in the acquired load data of the distribution feeder, so that an abnormal analysis result of the load data of the distribution feeder is inaccurate, and further, a large error occurs in abnormal early warning of the digital distribution feeder monitoring terminal.
Therefore, how to improve the result of performing anomaly analysis on load data of a distribution feeder becomes a problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides an automatic protection method for a digital distribution feeder monitoring terminal based on fault identification, so as to solve the problem of how to improve the result of carrying out abnormal analysis on load data of a distribution feeder.
The embodiment of the invention provides a digital distribution feeder monitoring terminal automatic protection method based on fault identification, which comprises the following steps:
collecting load data of a distribution feeder line in a preset time period in real time to form a load data time sequence, and screening the load data in the load data time sequence by utilizing a preset normal load data range to obtain initial abnormal load data with a time stamp of the distribution feeder line in the preset time period;
for any initial abnormal load data, according to the difference between the initial abnormal load data and a preset normal load threshold value, acquiring the abnormal fluctuation degree of the initial abnormal load data, and according to the abnormal fluctuation degree of all initial abnormal load data, filtering noise data in all initial abnormal load data to acquire target abnormal load data;
for any target abnormal load data, according to local load data of the target abnormal load data in the preset time period, acquiring continuous change trend indexes of the target abnormal load data, according to time stamps of each target abnormal load data in the local load data of the target abnormal load data, acquiring frequency characteristic values of the target abnormal load data, and according to the continuous change trend indexes and the frequency characteristic values, acquiring early warning degrees of the target abnormal load data;
and acquiring the early warning degree of all the target abnormal load data, and carrying out automatic protection on the digital distribution feeder monitoring terminal according to the early warning degree of all the target abnormal load data.
Further, the obtaining the abnormal fluctuation degree of the initial abnormal load data according to the difference between the initial abnormal load data and the preset normal load threshold includes:
acquiring a first difference absolute value between the initial abnormal load data and the maximum normal load threshold value, and acquiring a second difference absolute value between the initial abnormal load data and the minimum normal load threshold value;
and obtaining a minimum value between the first difference absolute value and the second difference absolute value, and carrying out negative mapping on the minimum value, wherein the obtained negative mapping result is used as the abnormal fluctuation degree of the initial abnormal load data.
Further, filtering noise data in all initial abnormal load data according to the abnormal fluctuation degree of all initial abnormal load data to obtain target abnormal load data, including:
acquiring an abnormal fluctuation degree threshold value for filtering noise data, and when the abnormal fluctuation degree of any initial abnormal load data is larger than the abnormal fluctuation degree threshold value, confirming that the initial abnormal load data is target abnormal load data;
and traversing all the initial abnormal load data to obtain all the target abnormal load data.
Further, the obtaining, according to the local load data of the target abnormal load data in the preset time period, a continuous variation trend indicator of the target abnormal load data includes:
in the load data time sequence within the preset time, taking the time stamp of the target abnormal load data as the center, and acquiring all load data within the preset time stamp range to form local load data of the target abnormal load data within the preset time period;
respectively calculating the absolute value of the difference between every two adjacent local load data in all local load data to obtain the average value of the absolute values of the differences;
acquiring front adjacent load data of the target abnormal load data in the load data time sequence, and calculating a third difference absolute value between the target abnormal load data and the front adjacent load data;
calculating the difference value between the average value of the absolute value of the difference value and the absolute value of the third difference value, and carrying out inverse proportion normalization processing on the opposite number of the difference value to obtain a corresponding normalization processing result;
counting the total number of the local load data and the first number of the target abnormal load data contained in the local load data, and obtaining the ratio of the total number to the first number;
and carrying out weighted summation on the normalization processing result and the ratio, wherein the obtained result is used as a continuous change trend index of the target abnormal load data.
Further, the obtaining the frequency characteristic value of the target abnormal load data according to the timestamp of each target abnormal load data in the local load data of the target abnormal load data includes:
any target abnormal load data in the local load data of the target abnormal load data is used as current load data, target abnormal load data adjacent to the current load data is obtained, a time interval between time stamps corresponding to the current load data and the target abnormal load data adjacent to the current load data is calculated, and if the time interval meets a preset time interval condition, the current load data is determined to be the target data;
and counting a second quantity of target data in the local load data of the target abnormal load data, obtaining a ratio between the second quantity and the total quantity of the local load data of the target abnormal load data, and taking a difference value between a preset value and the ratio as a frequency characteristic value of the target abnormal load data.
Further, the obtaining the early warning degree of the target abnormal load data according to the continuous variation trend index and the frequency characteristic value includes:
and respectively obtaining weights of the continuous variation trend index and the frequency characteristic value, and carrying out weighted summation on the continuous variation trend index and the frequency characteristic value, wherein an obtained weighted summation result is the early warning degree of the target abnormal load data.
Further, the automatic protection of the digital distribution feeder monitoring terminal according to the early warning degree of all the target abnormal load data comprises the following steps:
and acquiring a preset early warning degree threshold, comparing the early warning degree of all target abnormal load data with the preset early warning degree threshold to obtain a comparison result, and performing automatic protection of the digital distribution feeder monitoring terminal according to the comparison result.
Further, comparing the early warning degree of all the target abnormal load data with the preset early warning degree threshold to obtain a comparison result, including:
for any target abnormal load data, when the early warning degree of the target abnormal load data is greater than or equal to the preset early warning degree threshold value, the target abnormal load data is reserved;
and forming a comparison result by all the reserved target abnormal load data.
The embodiment of the invention has at least the following beneficial effects:
according to the invention, the initial abnormal load data is obtained by screening the load data in the acquired load data time sequence, so that the load data with excessive or insufficient load is primarily screened out, then, for any initial abnormal load data, the abnormal fluctuation degree of the initial abnormal load data is obtained by analyzing the difference between the initial abnormal load data and the normal load data, the noise data formed by noise interference in the acquisition process is filtered, the suspected abnormal load data (namely, the target abnormal load data) of the power distribution feeder is correspondingly obtained, and further, the early warning degree of each suspected abnormal load data is obtained based on the persistence or trend of the load change of the actual abnormal load data, so that the monitoring accuracy of the digital power distribution feeder monitoring terminal according to the early warning degree of all the suspected abnormal load data is improved, and the automatic protection processing of the digital power distribution feeder monitoring terminal is realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of steps of an automatic protection method for a digital distribution feeder monitoring terminal based on fault recognition according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects thereof of the digital distribution feeder monitoring terminal automatic protection method based on fault identification according to the invention, which is provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a digital distribution feeder line monitoring terminal automatic protection method based on fault identification, which is concretely described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for protecting a digital distribution feeder monitoring terminal based on fault recognition according to an embodiment of the present invention is shown, where the method includes the following steps:
step S101, load data of the distribution feeder line in a preset time period are collected in real time to form a load data time sequence, and the load data in the load data time sequence are screened by utilizing a preset normal load data range to obtain initial abnormal load data with time stamps of the distribution feeder line in the preset time period.
Specifically, the embodiment of the invention installs a sensor or load monitoring equipment on the pre-monitored distribution feeder to collect load data in real time, and transmits the load data collected in real time to a central server or a cloud platform for centralized storage and processing by adopting the internet of things (Lot) technology, so as to realize the automatic protection of the digital distribution feeder monitoring terminal, and particularly see below.
The load data in the embodiment of the invention includes, but is not limited to, one of other load indexes such as current a, voltage U, power W, etc. Depending on the particular type of load monitoring equipment installed on the distribution feeder.
According to the embodiment of the invention, the load data of the distribution feeder line in a preset time period are acquired in real time according to the load monitoring equipment installed on the distribution feeder line, all the load data acquired in the preset time period are formed into a load data time sequence according to the sequence of sampling time, then the load data in the load data time sequence are screened out by utilizing a preset normal load data range, so that the load data which are not in the normal load data range are screened out, the screened load data are regarded as abnormal load data with overlarge load or overlarge load, and the initial abnormal load data with time stamps of the distribution feeder line in the preset time period can be obtained. Wherein the preset time period is 1 hour, and the sampling frequency is 1 second, which is not limited in the embodiment of the present invention.
It should be noted that, the preset normal load data range can be set according to the safe power consumption standard of the distribution feeder, and the collected load data types are different, and the corresponding normal load data ranges are different, that is, the load current corresponds to the normal load current range and the load voltage corresponds to the normal load voltage range.
Step S102, aiming at any initial abnormal load data, acquiring the abnormal fluctuation degree of the initial abnormal load data according to the difference between the initial abnormal load data and a preset normal load threshold value, and filtering noise data in all initial abnormal load data according to the abnormal fluctuation degree of all initial abnormal load data to obtain target abnormal load data.
Specifically, in the process of acquiring load data by using the monitoring device, interference caused by multiple factors may occur, for example, measurement errors are introduced due to a certain precision or calibration problem of the monitoring device, or discontinuous and irregular noise data occurs in the data due to transmission errors or missing data packets and other problems in the process of data acquisition and transmission, meanwhile, noise data also occurs due to external interference such as electromagnetic radiation interference, power supply fluctuation and the like in the load monitoring environment, most of the data can be identified as abnormal load data points in the traditional monitoring mode, and serious influence is brought to final monitoring results and protection processing, so that noise data in initial abnormal load data (noise data caused by a certain precision or calibration problem of the monitoring device, or discontinuous and irregular noise data occurs in the data due to transmission errors or missing data packets and other problems in the process of data acquisition and transmission) needs to be filtered, so as to preserve the abnormal load data corresponding to suspected load abnormality.
Under normal operating conditions of the distribution feeder, the load of the distribution feeder will generally remain relatively stable. The amplitude change with smaller load value is probably caused by tiny fluctuation or noise of the system and is irrelevant to normal operation of a distribution feeder line, but for the amplitude change with larger load value, real abnormal events such as abnormal conditions, faults and the like of the system are more likely to be reflected, so that the probability that the value change of abnormal load data is smaller is noise data is extremely high, the probability that the value change of abnormal load data is larger is real load abnormality is higher, and based on the characteristics, the embodiment of the invention obtains the abnormal fluctuation degree of the initial abnormal load data according to the difference between the initial abnormal load data and a preset normal load threshold value aiming at any initial abnormal load data.
Preferably, the preset normal load threshold includes a maximum normal load threshold and a minimum normal load threshold, and the acquiring the abnormal fluctuation degree of the initial abnormal load data according to the difference between the initial abnormal load data and the preset normal load threshold includes:
acquiring a first difference absolute value between the initial abnormal load data and the maximum normal load threshold value, and acquiring a second difference absolute value between the initial abnormal load data and the minimum normal load threshold value;
and obtaining a minimum value between the first difference absolute value and the second difference absolute value, and carrying out negative mapping on the minimum value, wherein the obtained negative mapping result is used as the abnormal fluctuation degree of the initial abnormal load data.
In one embodiment, the calculation expression of the degree of abnormal fluctuation of any initial abnormal load data is:
wherein,indicating the degree of abnormal fluctuation of the initial abnormal load data corresponding to the ith time stamp, +.>Initial abnormal load data corresponding to the ith time stamp,/->Representing the maximum normal load threshold,/for the load>Representing a minimum normal load threshold value,taking the minimum function +_>An exponential function based on a natural constant e is represented.
It should be noted that the number of the substrates,representing that by calculating the absolute value of the difference between the initial abnormal load data corresponding to the ith time stamp and the maximum normal load threshold value and the minimum normal load threshold value respectively, the minimum absolute value of the difference is obtained to represent the variation difference of the initial abnormal load data corresponding to the ith time stamp,the smaller the value of (i) is, the description that the initial abnormal load data corresponding to the ith time stamp is noise dataThe higher the energy, the more the exponential function based on the natural constant e is used>For a pair ofNegative mapping is performed such that the mapping result +.>Monotonically decreasing between the value ranges 0-1, then +.>The more the value of (2) tends to 1, the higher the likelihood that the initial abnormal load data corresponding to the i-th time stamp is noise data.
So far, the abnormal fluctuation degree of each initial abnormal load data is respectively acquired by using the method for acquiring the abnormal fluctuation degree. After obtaining the abnormal fluctuation degrees of all initial abnormal load data, filtering noise data in all initial abnormal load data according to the abnormal fluctuation degrees of all initial abnormal load data to obtain target abnormal load data, wherein the specific method comprises the following steps: acquiring an abnormal fluctuation degree threshold value for filtering noise data, and when the abnormal fluctuation degree of any initial abnormal load data is larger than the abnormal fluctuation degree threshold value, confirming that the initial abnormal load data is target abnormal load data; and traversing all the initial abnormal load data to obtain all the target abnormal load data.
In one embodiment, the threshold value of the abnormal fluctuation degree is set to be 0.8, and if the abnormal fluctuation degree of the initial abnormal load data corresponding to the ith time stamp is greater than 0.8, the initial abnormal load data corresponding to the ith time stamp is confirmed to be noise data, and the initial abnormal load data corresponding to the ith time stamp is deleted.
Step S103, for any target abnormal load data, according to the local load data of the target abnormal load data in a preset time period, obtaining a continuous change trend index of the target abnormal load data, according to the time stamp of each target abnormal load data in the local load data of the target abnormal load data, obtaining a frequency characteristic value of the target abnormal load data, and according to the continuous change trend index and the frequency characteristic value, obtaining the early warning degree of the target abnormal load data.
Specifically, after deleting noise data in the initial abnormal load data, it is necessary to further perform feature analysis and abnormality evaluation on the retained target abnormal load data. If the load change characteristics frequently occur in a short time and have no obvious persistence and trend, the characteristics of noise data (noise data formed by external interference such as electromagnetic radiation interference and power supply fluctuation in a load monitoring environment) are likely to be met; in contrast, if the variation of the load abnormality has a certain persistence or trend and occurs in a longer period of time, the variation is more likely to be a real load abnormality, so that in order to enable the digital distribution feeder line monitoring terminal to accurately early warn, the early warning degree of each target abnormal load data is analyzed.
In the embodiment of the invention, for any target abnormal load data, according to local load data of the target abnormal load data in a preset time period, a continuous variation trend index of the target abnormal load data is acquired, and the specific acquisition method is as follows:
in the load data time sequence within the preset time, taking the time stamp of the target abnormal load data as the center, and acquiring all load data within the preset time stamp range to form local load data of the target abnormal load data within the preset time period;
respectively calculating the absolute value of the difference between every two adjacent local load data in all local load data to obtain the average value of the absolute values of the differences;
acquiring front adjacent load data of the target abnormal load data in the load data time sequence, and calculating a third difference absolute value between the target abnormal load data and the front adjacent load data;
calculating the difference value between the average value of the absolute value of the difference value and the absolute value of the third difference value, and carrying out inverse proportion normalization processing on the opposite number of the difference value to obtain a corresponding normalization processing result;
counting the total number of the local load data and the first number of the target abnormal load data contained in the local load data, and obtaining the ratio of the total number to the first number;
and carrying out weighted summation on the normalization processing result and the ratio, wherein the obtained result is used as a continuous change trend index of the target abnormal load data.
In one embodiment, the calculation expression of the continuous variation trend index of any target abnormal load data is:
wherein,continuous change trend index indicating that jth time stamp corresponds to target abnormal load data, ++>First quantity of target abnormal load data contained in local load data representing target abnormal load data corresponding to jth time stamp,/for>Representing any target abnormal load data, +.>Representing an exponential function based on a natural constant e, < ->Indicating that the jth timestamp corresponds to the target abnormal load data,/->The j-1 th time stamp of the time stamps representing the time sequence of the load data corresponds to the load data,/for each time stamp>Indicating the total amount of partial load data of the jth timestamp corresponding to the target abnormal load data, ++>Represents k+1th load data in the partial load data,>represents the kth load data in the local load data, < ->For the first weight, ++>For the second weight, the embodiment of the invention shows the empirical value +.>。
Preferably, in the embodiment of the present invention, the total amount of local load data corresponding to the target abnormal load data with respect to the jth timestampThe acquisition method of (1) comprises the following steps: in the load data time sequence, the local load data corresponding to the target abnormal load data, namely 6 local load data, of the j-th time stamp is formed by taking the target abnormal load data corresponding to the j-th time stamp as a center and acquiring 3 adjacent load data before and after the target abnormal load data corresponding to the j-th time stamp.
It should be noted that the number of the substrates,the method comprises the steps of representing the duty ratio of target abnormal load data in local load data corresponding to target abnormal load data of a jth time stamp, wherein the larger the duty ratio is, the higher the continuous abnormality in the local time range of the target abnormal load data corresponding to the jth time stamp is; />For characterizing the jth timestamp correspondence targetDifferences between the abnormal load data and the load data of its previous moment, < >>Characterizing a difference standard between two adjacent load data in a local time range of the target abnormal load data corresponding to the jth timestamp, thereby calculatingThe change trend of the target abnormal load data corresponding to the jth time stamp is represented, the smaller the value is, the change of the regularity difference is shown between the target abnormal load data corresponding to the jth time stamp and the load data before the target abnormal load data, or the change of the regularity difference is the same, the stronger the change trend of the regularity change of the target abnormal load data corresponding to the jth time stamp is further represented, otherwise, the larger the value is, the larger the change difference between the target abnormal load data corresponding to the jth time stamp and the load data before the target abnormal load data is represented, the change disorder of the target abnormal load data corresponding to the jth time stamp is represented, the change trend of the regularity change of the target abnormal load data corresponding to the jth time stamp is weaker, and therefore the change trend of the regularity change of the target abnormal load data corresponding to the jth time stamp is not provided>And the value of (2) and the continuous variation trend index are in negative correlation.
Further, in the embodiment of the present invention, for any target abnormal load data, according to a timestamp of each target abnormal load data in local load data of the target abnormal load data, a frequency characteristic value of the target abnormal load data is obtained, and specifically, the method for obtaining the frequency characteristic value of the target abnormal load data is as follows:
any target abnormal load data in the local load data of the target abnormal load data is used as current load data, target abnormal load data adjacent to the current load data is obtained, a time interval between time stamps corresponding to the current load data and the target abnormal load data adjacent to the current load data is calculated, and if the time interval meets a preset time interval condition, the current load data is determined to be the target data;
and counting a second quantity of target data in the local load data of the target abnormal load data, obtaining a ratio between the second quantity and the total quantity of the local load data of the target abnormal load data, and taking a difference value between a preset value and the ratio as a frequency characteristic value of the target abnormal load data.
In one embodiment, the calculation expression of the frequency eigenvalue of any target abnormal load data is:
wherein,frequency characteristic value indicating that jth time stamp corresponds to target abnormal load data,/for example>A time stamp indicating an a-th target abnormal load data among the partial load data of the j-th time stamp corresponding to the target abnormal load data,time stamp of a+1th target abnormal load data in the partial load data representing the jth time stamp corresponding to the target abnormal load data,/for>Indicating that when the time interval between the (a) th target abnormal load data and the corresponding time stamp of the (a+1) th target abnormal load data adjacent to the (a) th target abnormal load data is larger than or equal to the preset continuous time interval, confirming the (a) th target abnormal load data as the target data,>the number of target data is represented, 1 being a preset value.
It should be noted that, the continuous time interval v is set to the sampling frequency of the load data,the local load data used for representing the target abnormal load data corresponding to the jth time stamp is characterized in that adjacent target abnormal load data belongs to a data duty ratio with discontinuous time, and if the data duty ratio is not 0; the fact that obvious frequency fluctuation exists in the target abnormal load data in the local load data corresponding to the target abnormal load data by the jth time stamp is indicated, namely the jth time stamp is higher in possibility that the target abnormal load data is noise data (noise data formed by external interference such as electromagnetic radiation interference and power supply fluctuation in a load monitoring environment), and the possibility that the target abnormal load data corresponding to the jth time stamp is real abnormal load data is smaller; conversely, if the data duty ratio is 0, it is considered that the target abnormal load data in the local load data corresponding to the target abnormal load data does not have the frequency fluctuation feature, and the greater the possibility that the target abnormal load data corresponding to the jth time stamp is the true abnormal load data, the frequency feature value of the target abnormal load data corresponding to the jth time stamp is +_>Data duty cycle->And has a negative correlation.
By using the method for acquiring the continuous variation trend index and the frequency characteristic value, the continuous variation trend index and the frequency characteristic value of each target abnormal load data can be acquired respectively.
Further, according to the continuous change trend index and the frequency characteristic value of each target abnormal load data, the early warning degree of each target abnormal load data is respectively obtained and used for representing the necessity of carrying out abnormal early warning on the target abnormal load data. The method for acquiring the early warning degree of the target abnormal load data according to any target abnormal load data in the embodiment of the invention comprises the following steps: and respectively obtaining weights of the continuous variation trend index and the frequency characteristic value, and carrying out weighted summation on the continuous variation trend index and the frequency characteristic value, wherein an obtained weighted summation result is the early warning degree of the target abnormal load data.
In one embodiment, the calculation expression of the early warning degree of any target abnormal load data is:
wherein,indicating the early warning degree of the target abnormal load data corresponding to the o-th time stamp, < >>Continuous change trend index indicating that the o-th time stamp corresponds to the target abnormal load data,/>Frequency characteristic value indicating that the o-th time stamp corresponds to the target abnormal load data,/or->Weight coefficient representing continuous variation trend index, < ->And a weight coefficient representing the frequency characteristic value.
Preferably, in the embodiment of the invention, the method is endowed according to the experience value。
The larger the continuous change trend index is, the more the corresponding target abnormal load data is the real abnormal load, the larger the frequency characteristic value is, the more the corresponding target abnormal load data is the real abnormal load, and the greater the early warning degree of the corresponding target abnormal load data is, so that the early warning degree of the target abnormal load data is in positive correlation with the continuous change trend index and the frequency characteristic value respectively.
Thus, the early warning degree of the abnormal load data of each target can be obtained.
And step S104, acquiring the early warning degree of all the target abnormal load data, and carrying out automatic protection on the digital distribution feeder monitoring terminal according to the early warning degree of all the target abnormal load data.
Specifically, the early warning degree of all the target abnormal load data is obtained by utilizing the step S103, and then the automatic protection of the digital distribution feeder monitoring terminal is performed according to the early warning degree of all the target abnormal load data.
Preferably, the automatic protection of the digital distribution feeder monitoring terminal according to the early warning degree of all the target abnormal load data includes:
and acquiring a preset early warning degree threshold, comparing the early warning degree of all target abnormal load data with the preset early warning degree threshold to obtain a comparison result, and performing automatic protection of the digital distribution feeder monitoring terminal according to the comparison result.
Specifically, in the embodiment of the present invention, the early warning degree threshold is 0.7, which is not limited by the present invention. According to the comparison result, the digital distribution feeder monitoring terminal is automatically protected, so that the sensitivity of the digital distribution feeder monitoring terminal to noise load data is reduced, and the sensitivity to data points with higher early warning degree is increased.
Preferably, the comparing the early warning degree of all the target abnormal load data with the preset early warning degree threshold value to obtain a comparison result includes:
for any target abnormal load data, when the early warning degree of the target abnormal load data is greater than or equal to the preset early warning degree threshold value, the target abnormal load data is reserved;
and forming a comparison result by all the reserved target abnormal load data.
Specifically, the target abnormal load data are distinguished according to the early warning degree, the early warning processing is carried out on the target abnormal load data with high early warning degree, and the target abnormal load data with low early warning degree are ignored, so that if the early warning degree of any one target abnormal load data is greater than or equal to 0.7, the target abnormal load data are reserved, and further, the comparison result of all reserved target abnormal load data can be obtained.
In summary, the embodiment of the invention collects the load data of the distribution feeder line in the preset time period in real time to form a load data time sequence, and screens the load data in the load data time sequence by utilizing the preset normal load data range to obtain the initial abnormal load data with the timestamp of the distribution feeder line in the preset time period; for any initial abnormal load data, according to the difference between the initial abnormal load data and a preset normal load threshold value, acquiring the abnormal fluctuation degree of the initial abnormal load data, and according to the abnormal fluctuation degree of all initial abnormal load data, filtering noise data in all initial abnormal load data to acquire target abnormal load data; for any target abnormal load data, according to local load data of the target abnormal load data in the preset time period, acquiring continuous change trend indexes of the target abnormal load data, according to time stamps of each target abnormal load data in the local load data of the target abnormal load data, acquiring frequency characteristic values of the target abnormal load data, and according to the continuous change trend indexes and the frequency characteristic values, acquiring early warning degrees of the target abnormal load data; and acquiring the early warning degree of all the target abnormal load data, and carrying out automatic protection on the digital distribution feeder monitoring terminal according to the early warning degree of all the target abnormal load data. The method comprises the steps of screening load data in a collected load data time sequence to obtain initial abnormal load data so as to preliminarily screen out load data with excessive or insufficient load, then, aiming at any initial abnormal load data, obtaining abnormal fluctuation degree of the initial abnormal load data by analyzing the difference between the initial abnormal load data and normal load data, filtering noise data formed by noise interference in the collection process, and correspondingly obtaining suspected abnormal load data (namely target abnormal load data) of a power distribution feeder, thereby obtaining early warning degree of each suspected abnormal load data based on the persistence or trend of load change of actual abnormal load data, improving the monitoring accuracy of a digital power distribution feeder monitoring terminal according to the early warning degree of all the suspected abnormal load data, and realizing automatic protection processing of the digital power distribution feeder monitoring terminal.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. The automatic protection method for the digital distribution feeder monitoring terminal based on fault identification is characterized by comprising the following steps of:
collecting load data of a distribution feeder line in a preset time period in real time to form a load data time sequence, and screening the load data in the load data time sequence by utilizing a preset normal load data range to obtain initial abnormal load data with a time stamp of the distribution feeder line in the preset time period;
for any initial abnormal load data, according to the difference between the initial abnormal load data and a preset normal load threshold value, acquiring the abnormal fluctuation degree of the initial abnormal load data, and according to the abnormal fluctuation degree of all initial abnormal load data, filtering noise data in all initial abnormal load data to acquire target abnormal load data;
for any target abnormal load data, according to local load data of the target abnormal load data in the preset time period, acquiring continuous change trend indexes of the target abnormal load data, according to time stamps of each target abnormal load data in the local load data of the target abnormal load data, acquiring frequency characteristic values of the target abnormal load data, and according to the continuous change trend indexes and the frequency characteristic values, acquiring early warning degrees of the target abnormal load data;
acquiring early warning degrees of all target abnormal load data, and performing automatic protection of the digital distribution feeder monitoring terminal according to the early warning degrees of all target abnormal load data;
the obtaining the continuous variation trend index of the target abnormal load data according to the local load data of the target abnormal load data in the preset time period comprises the following steps:
in the load data time sequence in the preset time period, taking the time stamp of the target abnormal load data as the center, and acquiring all load data in the preset time stamp range to form local load data of the target abnormal load data in the preset time period;
respectively calculating the absolute value of the difference between every two adjacent local load data in all local load data to obtain the average value of the absolute values of the differences;
acquiring front adjacent load data of the target abnormal load data in the load data time sequence, and calculating a third difference absolute value between the target abnormal load data and the front adjacent load data;
calculating the difference value between the average value of the absolute value of the difference value and the absolute value of the third difference value, and carrying out inverse proportion normalization processing on the opposite number of the difference value to obtain a corresponding normalization processing result;
counting the total number of the local load data and the first number of the target abnormal load data contained in the local load data, and obtaining the ratio of the total number to the first number;
weighting and summing the normalization processing result and the ratio to obtain a result serving as a continuous change trend index of the target abnormal load data;
the obtaining the frequency characteristic value of the target abnormal load data according to the time stamp of each target abnormal load data in the local load data of the target abnormal load data comprises the following steps:
any target abnormal load data in the local load data of the target abnormal load data is used as current load data, target abnormal load data adjacent to the current load data is obtained, a time interval between time stamps corresponding to the current load data and the target abnormal load data adjacent to the current load data is calculated, and if the time interval meets a preset time interval condition, the current load data is determined to be the target data;
and counting a second quantity of target data in the local load data of the target abnormal load data, obtaining a ratio between the second quantity and the total quantity of the local load data of the target abnormal load data, and taking a difference value between a preset value and the ratio as a frequency characteristic value of the target abnormal load data.
2. The method for automatically protecting a digital distribution feeder monitoring terminal based on fault identification according to claim 1, wherein the preset normal load threshold includes a maximum normal load threshold and a minimum normal load threshold, and the step of obtaining the abnormal fluctuation degree of the initial abnormal load data according to the difference between the initial abnormal load data and the preset normal load threshold includes:
acquiring a first difference absolute value between the initial abnormal load data and the maximum normal load threshold value, and acquiring a second difference absolute value between the initial abnormal load data and the minimum normal load threshold value;
and obtaining a minimum value between the first difference absolute value and the second difference absolute value, and carrying out negative mapping on the minimum value, wherein the obtained negative mapping result is used as the abnormal fluctuation degree of the initial abnormal load data.
3. The method for automatically protecting the digital distribution feeder monitoring terminal based on fault identification as claimed in claim 1, wherein the filtering noise data in all initial abnormal load data according to the abnormal fluctuation degree of all initial abnormal load data to obtain target abnormal load data comprises the following steps:
acquiring an abnormal fluctuation degree threshold value for filtering noise data, and when the abnormal fluctuation degree of any initial abnormal load data is larger than the abnormal fluctuation degree threshold value, confirming that the initial abnormal load data is target abnormal load data;
and traversing all the initial abnormal load data to obtain all the target abnormal load data.
4. The method for automatically protecting the digital distribution feeder monitoring terminal based on fault identification according to claim 1, wherein the step of obtaining the early warning degree of the target abnormal load data according to the continuous variation trend index and the frequency characteristic value comprises the following steps:
and respectively obtaining weights of the continuous variation trend index and the frequency characteristic value, and carrying out weighted summation on the continuous variation trend index and the frequency characteristic value, wherein an obtained weighted summation result is the early warning degree of the target abnormal load data.
5. The method for automatically protecting the digital distribution feeder monitoring terminal based on fault identification according to claim 1, wherein the automatically protecting the digital distribution feeder monitoring terminal according to the early warning degree of all target abnormal load data comprises the following steps:
and acquiring a preset early warning degree threshold, comparing the early warning degree of all target abnormal load data with the preset early warning degree threshold to obtain a comparison result, and performing automatic protection of the digital distribution feeder monitoring terminal according to the comparison result.
6. The method for automatically protecting a digital distribution feeder monitoring terminal based on fault identification as claimed in claim 5, wherein comparing the pre-warning degree of all target abnormal load data with the pre-set pre-warning degree threshold to obtain a comparison result comprises
For any target abnormal load data, when the early warning degree of the target abnormal load data is greater than or equal to the preset early warning degree threshold value, the target abnormal load data is reserved;
and forming a comparison result by all the reserved target abnormal load data.
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